Artificial Intelligence: Beyond the Hype

The conversation around Artificial Intelligence (I'll simply call it AI from now on) is everywhere. It's discussed at conferences, seminars, trade shows, industry forums, across social media and traditional media, but also over coffee, at dinner tables, in boardrooms, classrooms, and family gatherings.

That alone tells us something important: AI is widely recognized as a transformative force, one that deserves open, informed discussion...

Those three dots are intentional. They lead to an essential question.

Are we truly prepared to have this conversation?

Do we understand the fundamental concepts behind AI, or are we behaving as we often do in other fields - becoming sports coaches while watching a game or legal experts whenever a headline makes the news?

This question comes to mind every time I listen to a debate or read an article about AI. Sometimes I'm genuinely surprised. Other times, I find myself discouraged.

What concerns me most is the widespread confusion surrounding some of the technology's most basic concepts.

• To begin with, many people equate Artificial Intelligence with public Large Language Models (LLMs).

  In reality, LLMs are only one application of AI - an important one, certainly - but they do not represent AI as a whole.

• Another common misconception is the tendency to treat what is fundamentally a statistical, mathematical, and probabilistic system as if it were a thinking entity. Personally, I still find the adjective "intelligent" somewhat misleading when applied to a machine. It's a useful shorthand, but it should never be taken literally.

• There is also a widespread belief that AI can only be accessed through online services such as ChatGPT, Copilot, Gemini, or similar platforms - and often only through paid subscriptions. The reality is quite different.

  Since the Dartmouth Conference in 1956, AI has been regarded as a branch of computer science designed to serve humanity. Today, many AI models are released under open licenses, allowing developers, researchers, companies, and even curious enthusiasts to download, study, and run them locally. We should clearly distinguish between AI models, which are often openly available, and commercial services, which package those models into convenient cloud-based products with specific pricing models.

  Implementing solutions in-house, hosted, or in the cloud is a business choice, not a technical or market constraint.

  Protecting corporate knowledge is neither a theoretical exercise nor an overwhelming investment. It is a conscious business decision.

• Another persistent myth is that AI only works when fed with enormous amounts of data - the well-known notion of Big Data. While some applications certainly require massive datasets, countless successful business projects demonstrate that effective AI solutions can also be built using much smaller, well-targeted collections of data.

• This misconception naturally leads to another: the belief that data must be perfectly clean before AI can generate value. In theory, better data produces better results. In practice, however, AI is increasingly being used to improve data quality itself by identifying anomalies, correcting inconsistencies, enriching datasets, and supporting data preparation for Machine Learning and predictive analytics.

• Finally, there is the assumption that AI requires enormous computing infrastructures and expensive cloud environments. Once again, this is only partially true. Some advanced models certainly demand significant computational power, but thousands of AI applications are successfully running on appropriately sized on-premises servers and affordable enterprise infrastructures, without relying on hyperscale cloud platforms.

These misconceptions create uncertainty, especially within the business community. And uncertainty often leads to hesitation.

Should we invest now? Should we wait? Are we moving too early - or already too late?

Ironically, this uncertainty can become the greatest obstacle to innovation itself.

That brings me to a simple recommendation for every business leader and decision-maker.

Before listening to the promises of the market, invest a few hours in understanding the fundamentals of Artificial Intelligence.

You don't need to become a programmer. You don't need a degree in mathematics or data science. What you do need is a solid understanding of the basic principles: how AI works, what problems it can solve, where its limitations lie, and which of the many popular beliefs are simply misconceptions.

Just a few hours of well-structured education can eliminate much of the confusion and provide the confidence needed to evaluate opportunities with clarity.

Only then can we distinguish real opportunities from marketing hype.